Fine-tune Any LLM from the Hugging Face Hub with Together AI

The pace of AI development today is breathtaking. Every single day, hundreds of new models appear on the Hugging Face Hub , some are specialized variants of popular base models like Llama or Qwen, others feature novel architectures or have been trained from scratch for specific domains. Whether it's a medical AI trained on clinical data, a coding assistant optimized for a particular programming language, or a multilingual model fine-tuned for specific cultural contexts, the Hugging Face Hub has become the beating heart of open-source AI innovation.

But here's the challenge: finding an amazing model is just the beginning. What happens when you discover a model that's 90% perfect for your use case, but you need that extra 10% of customization? Traditional fine-tuning infrastructure is complex, expensive, and often requires significant DevOps expertise to set up and maintain.

This is exactly the gap that Together AI and Hugging Face are bridging today. We're announcing a powerful new capability that makes the entire Hugging Face Hub available for fine-tuning using Together AI's infrastructure. Now, any compatible LLM on the Hub, whether it's from Meta or an individual contributor, can be fine-tuned with the same ease and reliability you expect from Together's platform.🚀

Think of the base model as a "training template." It tells our system how to optimally allocate GPU resources, configure memory usage, set up the training pipeline, and prepare the model for inference. Your custom model should have a similar architecture, approximate size, and sequence length to the base model for optimal results.

As seen in the example above, if you want to fine-tune HuggingFaceTB/SmolLM2-1.7B-Instruct (which uses Llama architecture), you'd use togethercomputer/llama-2-7b-chat as your base model template, because they share the same underlying architecture.

Perhaps most importantly, this approach gives teams access to the collective intelligence of the open-source community. Every breakthrough, every specialized adaptation, every novel architecture becomes a potential starting point for their own work.

Beta users and early adopters of this feature are already seeing results across diverse use cases.

That's it! Your model will be trained on Together's infrastructure and can be deployed for inference, downloaded or even uploaded back to the Hub! For private repositories, simply add your HF token with hf_api_token="hf_xxxxxxxxxxxx" .

As seen in the example above, when you fine-tune a Hugging Face model on Together AI, you actually specify two models:

The integration works bidirectionally . Together AI can pull any compatible public model from the Hugging Face Hub for training, and with the proper API tokens, it can download models private repositories as well. After training, your fine-tuned model can be automatically pushed back onto the Hub if you've specified hf_output_repo_name , making it available for sharing with your team or the broader community.